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Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence

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Computational Drug Discovery and Design

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2714))

Abstract

Peptides modulate many processes of human physiology targeting ion channels, protein receptors, or enzymes. They represent valuable starting points for the development of new biologics against communicable and non-communicable disorders. However, turning native peptide ligands into druggable materials requires high selectivity and efficacy, predictable metabolism, and good safety profiles. Machine learning models have gradually emerged as cost-effective and time-saving solutions to predict and generate new proteins with optimal properties. In this chapter, we will discuss the evolution and applications of predictive modeling and generative modeling to discover and design safe and effective antimicrobial peptides. We will also present their current limitations and suggest future research directions, applicable to peptide drug design campaigns.

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Acknowledgments

Mariana del Carmen Aguilera-Puga (M.C.A.-P.) and Fabien Plisson (F.P.) are thankful to the Mexican research council Consejo Nacional de Ciencia y Tecnología (CONACYT), grant number A1-S-32579 and Premio Rosenkranz 2021 (FunSalud - Fundación para la Salud, A.C. and Roche, AG). M.C.A.-P. was the recipient of national CONACYT postgraduate scholarship. F.P. was supported by a Cátedras CONACYT fellowship—2017–2022. Natalia L. Cancelarich (N.L.C.) was awarded a postdoctoral fellowship by the Argentinian research council Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). Mariela M. Marani (M.M.M.) is a researcher of CONICET. Cesar de la Fuente-Nunez (C.F.N.) holds a Presidential Professorship at the University of Pennsylvania, is a recipient of the Langer Prize by the AIChE Foundation, and acknowledges funding from the IADR Innovation in Oral Care Award, the Procter & Gamble Company, United Therapeutics, a BBRF Young Investigator Grant, the Nemirovsky Prize, Penn Health-Tech Accelerator Award, the Dean’s Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania, the National Institute of General Medical Sciences of the National Institutes of Health under award number R35GM138201, and the Defense Threat Reduction Agency (DTRA; HDTRA11810041, HDTRA1-21-1-0014, and HDTRA1-23-1-0001).

Competing Interests

C.F.N. provides consulting services to Invaio Sciences and is a member of the Scientific Advisory Boards of Nowture S.L. and Phare Bio. The remaining authors declare no competing interests.

Author Contributions

F.P. conceptualized the chapter. M.C.A.-P. and N.L.C. wrote the first draft. M.M.M., C.F.N., and F.P. wrote and edited the chapter. M.C.A.-P. created the tables and F.P. made the figures. All authors reviewed the manuscript.

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Authors and Affiliations

  1. Centro de Investigación y de Estudios Avanzados del IPN (CINVESTAV-IPN), Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Irapuato, Guanajuato, Mexico

    Mariana d. C. Aguilera-Puga & Fabien Plisson

  2. Instituto Patagónico para el Estudio de los Ecosistemas Continentales (IPEEC), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Puerto Madryn, Argentina

    Natalia L. Cancelarich & Mariela M. Marani

  3. Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Cesar de la Fuente-Nunez

  4. Departments of Bioengineering and Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA

    Cesar de la Fuente-Nunez

  5. Penn Institute for Computational Science, University of Pennsylvania, Philadelphia, PA, USA

    Cesar de la Fuente-Nunez

  6. CINVESTAV-IPN, Unidad Irapuato, Departamento de Biotecnología y Bioquímica, Irapuato, Guanajuato, Mexico

    Mariana d. C. Aguilera-Puga & Fabien Plisson

Authors
  1. Mariana d. C. Aguilera-Puga
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  2. Natalia L. Cancelarich
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  3. Mariela M. Marani
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  4. Cesar de la Fuente-Nunez
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  5. Fabien Plisson
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Corresponding authors

Correspondence to Cesar de la Fuente-Nunez or Fabien Plisson .

Editor information

Editors and Affiliations

  1. Department of Basic and Applied Sciences, Dayananda Sagar University, Bangalore, Karnataka, India

    Mohini Gore

  2. Department of Botany, Rajaram College Kolhapur, Kolhapur, Maharashtra, India

    Umesh B. Jagtap

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Aguilera-Puga, M.d.C., Cancelarich, N.L., Marani, M.M., de la Fuente-Nunez, C., Plisson, F. (2024). Accelerating the Discovery and Design of Antimicrobial Peptides with Artificial Intelligence. In: Gore, M., Jagtap, U.B. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 2714. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3441-7_18

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  • DOI: https://doi.org/10.1007/978-1-0716-3441-7_18

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